基于红外热成像的CFRP复合材料低速冲击损伤表征

朱笑; 袁丽华

doi:10.13801/j.cnki.fhclxb.20210831.001

基于红外热成像的CFRP复合材料低速冲击损伤表征

doi: 10.13801/j.cnki.fhclxb.20210831.001

朱笑,
袁丽华^,

南昌航空大学　无损检测教育部重点实验室，南昌 330063

基金项目: 国家自然科学基金(51865038)；江西省研究生创新专项(YC2020-S547)

详细信息

通讯作者:
袁丽华，博士，副教授，硕士生导师，研究方向为红外无损检测　E-mail: lihuayuan@nchu.edu.cn

中图分类号: TB332
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出版历程
- 收稿日期: 2021-07-15
- 修回日期: 2021-08-11
- 录用日期: 2021-08-14
- 网络出版日期: 2021-08-31
- 刊出日期: 2022-08-31

Low-velocity impact damage characterization of CFRP composite based on infrared thermography

ZHU Xiao,
YUAN Lihua^,

Key Laboratory of Nondestructive Testing (Ministry of Education), Nanchang Hangkong University, Nanchang 330063, China

摘要

摘要: 采用红外热成像检测技术对20 J、40 J冲击载荷下的碳纤维增强树脂基(CFRP)复合材料层合板表面与内部损伤进行识别。针对内部缺陷定量提取不准确的问题，通过分析红外图像空域特征，提出一种多尺度八方向边缘检测图像分割算法。首先依据敏感区域最大标准差法选取最优图像并采用模糊C均值聚类算法对红外缺陷图像进行预分割获取先验信息，然后构建圆形卷积模板对红外图像进行多尺度八方向卷积运算，引入OTSU算法分割梯度图像，结合形态学运算得到缺陷边缘图，对目标区域进行连通域分析，实现缺陷特征的定量提取。研究结果表明，本文算法提高了损伤区域弱边缘的检测能力，保证了缺陷边缘的完整性与连通性，相较于传统图像分割方法在缺陷面积、长径、短径的检测精度上分别提升了20.41%、5.61%、9.77%以上。
- 脉冲热成像 /
- CFRP复合材料 /
- 低速冲击损伤 /
- 模糊C均值聚类 /
- 特征提取
Abstract: The surface and internal damage of carbon fiber reinforced polymer (CFRP) laminates under 20 J and 40 J impact loads were identified by infrared thermography. Aiming at the inaccuracy of quantitative defect extraction, a multi-scale eight-direction edge detection image segmentation algorithm was proposed by analyzing the spatial characteristics of infrared images. Firstly, the optimal image was selected according to the maximum standard deviation of the sensitive area, and the fuzzy C-means clustering algorithm was used to presegment the defect image to obtain prior information. Then a circular convolution template was constructed to perform a multi-scale eight-direction convolution operation on the infrared image. The OTSU algorithm was introduced to segment the gradient image, combined with morphological operations to obtain the defect edge map, and the connected domain of the target area was analyzed to realize the quantitative extraction of defect features. The results show that the proposed algorithm improves the detection ability of weak edges in the damaged area and ensures the integrity and connectivity of defect edges. Compared with the traditional image segmentation algorithm, the detection accuracy of the defect area, long diameter and short diameter obtained by the proposed algorithm are improved by more than 20.41%, 5.61% and 9.77%, respectively.
- pulsed thermography /
- CFRP composite /
- low-velocity impact damage /
- fuzzy C-means clustering /
- defect feature extraction

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图 1 透射法检测原理

Figure 1. Schematic diagram of transmission method

CFRP—Carbon fiber reinforced polymer; IR—Infrared radiation

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图 2 热波图像处理流程

Figure 2. Processing flow chart of infrared heat wave image

FCM—Fuzzy C-means

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图 3 实验装置

Figure 3. Experiment set-up

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图 4 CFRP板与损伤演化演示图

Figure 4. Schematic drawing of CFRP laminate and damage evolution

V—Velocity

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图 5 CFRP红外显微检测结果

Figure 5. Results of infrared photomicrogaphies for CFRP

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图 6 CFRP冲击损伤脉冲红外热波序列图

Figure 6. Pulse infrared heat wave images sequence of CFRP on impact damage

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图 7 CFRP多尺度八方向边缘检测分割算法框图

Figure 7. Block diagram of multi-scale eight-direction edge detection and segmentation method for CFRP

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图 8 CFRP红外图像分割效果图

Figure 8. Segmentation results by different segmentation algorithms for CFRP

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图 9 CFRP水浸超声C扫描检测结果

Figure 9. Detection results of ultrasonic immersion C-scan of CFRP

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表 1 CFRP缺陷特征定量提取结果

Table 1. Quantitative analysis results of defect characteristics of CFRP

Method	Area/mm²		Long diameter/mm		Short diameter/mm
Method	20 J	40 J	20 J	40 J	20 J	40 J
C-Scan	788.40	2356.23	43.70	83.60	23.94	40.30
OTSU	533.70	1657.92	38.14	73.94	19.36	31.69
Iterative threshold	530.63	1648.39	38.03	73.72	19.31	31.60
Genetic algorithm	539.19	1660.37	38.62	73.74	20.54	32.17
Region growing	492.31	1596.08	36.83	73.17	18.29	30.31
In this article	759.95	2141.47	41.07	79.20	22.88	37.55

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表 2 不同算法处理结果的相对误差

Table 2. Relative errors of the results processed by different algorithms

Algorithms	Relative error of area/%		Relative error of long diameter/%		Relative error of short diameter/%
Algorithms	20 J	40 J	20 J	40 J	20 J	40 J
OTSU	32.31	29.64	12.72	11.56	19.13	21.36
Iterative threshold	32.70	30.04	12.97	11.82	19.34	21.59
Genetic algorithm	31.61	29.53	11.63	11.79	14.20	20.17
Region growing	37.56	32.26	15.72	12.48	23.60	24.79
In this article	3.61	9.11	6.02	5.26	4.43	6.82

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